Analysis system and method used to construct social structures based on data collected from monitored web pages

ABSTRACT

Embodiments of a method for determining a mapping are illustrated. In an embodiment, the method includes receiving a log record from a tracking component that is located on a plurality of web pages. The method further includes determining a first mapping between a plurality of anchors associated with the plurality of users. The method also includes determining a second mapping between the plurality of users based on the first mapping.

TECHNICAL FIELD

The present disclosure relates, in general, to a data collection andanalysis system. More specifically, the present disclosure relates to adata collection system and analysis system used to construct socialstructures.

BACKGROUND

Global Internet usage has seen multifold growth due to exponentialincrease in number of Internet users. At any instant in time, there maybe millions of users involved in various activities on the Internet.Such activities can include, but are not limited to, searching forcontent, visiting a web page, viewing a video blog, social networking,listening to an audio file, shopping online, gaming online, sharingcontent, following friends or celebrities, and downloading content. Suchuser activities may be indicative of a user's interest and/or onlinebehavioral pattern.

SUMMARY

Embodiments of a method for determining a mapping between a plurality ofusers accessing a plurality of web pages are disclosed. In anembodiment, the method includes receiving at least one log record from atracking component located on the plurality of web pages. The at leastone log record corresponds to one or more activities of the plurality ofusers on the plurality of web pages. The method further includesdetermining a first mapping between a plurality of information anchorson the plurality of web pages and the plurality of users based on thecorresponding user activities. The plurality of information anchors areutilized to perform the one or more user activities such as, but are notlimited to, viewing the web page and sharing through the trackingcomponent. The method includes determining a second mapping between theplurality of users based on the first mapping. The second mappingincludes a plurality of nodes and a plurality of edges connecting thenodes. The plurality of nodes represents the plurality of users and theplurality of edges represents the plurality of information anchors.

BRIEF DESCRIPTION OF DRAWINGS

The following detailed description of the embodiments of the disclosedinvention will be better understood when read with reference to theappended drawings. The invention is illustrated by way of example, andis not limited by the accompanying figures, in which like referencesindicate similar elements.

FIG. 1 illustrates a system environment in which the present disclosurecan be implemented;

FIG. 2 illustrates an exemplary system diagram showing various modulesinvolved in operations of a web analytic server in accordance with anembodiment;

FIG. 3 illustrates a diagram showing a sharing graph in accordance withan embodiment;

FIG. 4 illustrates a flowchart showing a method for determining amapping in accordance with an embodiment;

FIG. 5 illustrates grouping share and click cookies by Unified ResourceLocator (URL) in accordance with an embodiment;

FIG. 6 a illustrates an input information received by a web analyticserver for linking share and click cookies with respect to URL inaccordance with an embodiment;

FIG. 6 b illustrates an output cookie graph generated by a web analyticserver by linking share and click cookies with respect to URL inaccordance with an embodiment;

FIG. 7 illustrates a method of disambiguating links between share andclick cookies by using URL as an anchor in accordance with anembodiment;

FIG. 8 illustrates a method of assigning numerical weights to graphedges in accordance with an embodiment;

FIG. 9 illustrates a graph showing a click back time within first day ofa shared content in accordance with an embodiment;

FIG. 10 a shows statistics of an average number of click eventsassociated with a URL for a first mapping between an anchor and a userin accordance with an embodiment;

FIG. 10 b shows statistics of an average number of share eventsassociated with a URL for a first mapping between an anchor and a userin accordance with an embodiment;

FIG. 11 a shows statistics of number of inbound clicks associated with auser for a second mapping between a plurality of users in accordancewith an embodiment;

FIG. 11 b shows statistics of number of outbound clicks associated witha user for a second mapping between a plurality of users in accordancewith an embodiment;

FIG. 12 illustrates a method of constructing a social structureincrementally in accordance with an embodiment; and

FIG. 13 illustrates an alternative method of constructing a socialstructure incrementally in accordance with yet another embodiment.

DETAILED DESCRIPTION

The present disclosure can be best understood when read with referenceto the detailed figures and description set forth herein. Variousembodiments are discussed below with reference to the figures. However,those skilled in the art will readily appreciate that the detaileddescription given herein with respect to these figures is just forexplanatory purposes as methods and systems of the invention extendbeyond the described embodiments. For example, those skilled in the artwill appreciate that, in light of the teachings presented, multiplealternative and suitable approaches can be recognized, depending on theneeds of a particular application, to implement the functionality of anydetail described herein.

DEFINITION OF TERMS

Social structure: A social structure corresponds to a structure thatrepresents a social relationship between users or user interactions. Anexample implementation of a social structure can be a social graph.

Social graph: A social graph corresponds to a graphical representationof links prevailing between users. The links are indicative of userrelations, such as sharing of similar interest (e.g. an interest graph),proximity of locations (e.g. location-based social networks), orcommunication connections (e.g. email networks).

Sharing graph: A sharing graph, a type of social graph, corresponds to agraphical representation of links prevailing between sharers andclickers (defined below). The sharing graph is indicative of arelationship between the sharers and the clickers. The sharing graphincludes nodes and edges. The nodes represent users and the edgesrepresent anchors connecting the users. For purposes of the ongoingdescription, a cookie can also represent a user.

Channel: A channel corresponds to a website through which a sharingactivity or a clicking activity takes places. For example,www.facebook.com represents a social networking channel, Facebook®.

Tracking URL: A tracking Unified Resource Locator (URL) corresponds to aURL that has the capability to encode information to identify uniqueuser events such as sharing and clicking events. For instance, thetracking URL can be useful to track the user who shares a URL and thosewho respond to the shared URL. An example of the tracking URL is ashortened URL.

Shortened URL: A shortened URL corresponds to a URL that is shorter inlength but leads a user to a webpage associated with the shortened URL.For example, the URL http://en.x11y22z33.org/mobile/models/sjagwsed canbe shortened to http://shar.es/HMiPz. In an instance, there can be anumber of shortened URLs that can be generated for a particular URL. Insuch cases, the shortened URLs can be used for tracking and identifyingunique user events as they encapsulate the information about a sharer, asharing channel used by the sharer for sharing and a sharing time.Examples of a shortened URL include, but are not limited to, a shar.esURL (a program available from ShareThis® for shortening a URL), and ahashed URL.

Sharer: A sharer corresponds to a user or a node that performs anoperation of sharing a particular information entity (e.g., a URL, ashortened URL of a web page, or copy and paste text snippets) with aplurality of users. For example, a sharer may correspond to a cookierepresenting a user. A sharer is interchangeably referred to as aninformation sharer.

Share frequency: A share frequency corresponds to a frequency at whichthe users share the particular information entity.

Clicker: A clicker corresponds to a user or a node that performs anoperation of clicking on a URL shared by a sharer on a web page. Forexample, a clicker may correspond to a cookie representing a user. Inmost cases, the clicker performs the operation of clicking on ashortened URL of the URL that is shared by the sharer. A clicker mayalso be referred to as an information responder.

Information anchor: An information anchor corresponds to data thatdetermines the link between an information sharer and an informationresponder. For example, an information anchor may correspond to ashortened URL, a shared URL, an event tracking URL, a copy and pastetext snippet, an interest topic, etc. An information anchor isinterchangeably referred to as an anchor.

Click back time: Click back time corresponds to a lag in time thatoccurs between a time of share of a particular URL and a time of clickback of the particular URL.

Tracking application: A tracking application corresponds to a softwareapplication which when installed on a web server results in an embeddedtracking component in a web page hosted by the web server.

Tracking component: A tracking component is a web-based component thatis part of a web page configured to gather log records. The log recordsfacilitate tracking of a user activity. Examples of the log record mayinclude, but are not limited to, an anonymous cookie representing one ormore users, a timestamp, an event type, a sharing channel, a contentidentifier, a domain information, and a browser agent. Examples of thetracking component include, but are not limited to, a widget, a button,a hypertext, a web beacon and a link.

User activity: A user activity corresponds to the activities of the useron a web page. Examples of user activities include, but are not limitedto, viewing a web page and sharing the webpage through a trackingcomponent. The user activities are stored as user activity data that hasusers represented as cookies.

FIG. 1 illustrates a system environment 100 in which the presentdisclosure can be implemented. The system environment 100 includes anetwork 102, a web analytic server 104, a social structure manager 106,a plurality of domain web servers 108 a, 108 b and 108 c (hereinafterreferred to as domain web server 108) and a tracking component 110. Thesystem environment 100 further includes a plurality of computing devices112 a, 112 b and 112 c (generally referred to as computing device 112),a database 114 and a plurality of web pages 116. Each of the pluralityof domain web servers (such as the domain web server 108) hosts aplurality of web pages 116. Each of the plurality of web pages 116comprises at least one tracking component 110.

The network 102 corresponds to a medium through which content andmessages flow between the various components (e.g. the computing device112, the web analytic server 104, the database 114, and the domain webserver 108) of the system environment 100. Examples of the network 102may include, but are not limited to, a television broadcasting system,an IPTV network, a Wireless Fidelity (Wi-Fi) network, a Wireless AreaNetwork (WAN), a Local Area Network (LAN) or a Metropolitan Area Network(MAN). Various devices in the system environment 100 can connect to thenetwork 102 in accordance with various wired and wireless communicationprotocols such as Transmission Control Protocol and Internet Protocol(TCP/IP), User Datagram Protocol (UDP) and 2G, 3G or 4 G communicationprotocols.

In an embodiment, the web analytic server 104 corresponds to a webanalytic system with capabilities to extract and analyze data forcommercial purposes. Further, the web analytic server 104 includesvarious analytical tools, such as the social structure manager 106,configured for uncovering relationship between users related to asharing event in a networked environment and for constructing a socialstructure. Such analytical tools may further include, but are notlimited to, a tracking tool, a social behavior analytic tool, a socialinfluence analytic tool, an audience segmentation tool, a user modelingtool, a campaign analytic tool, a campaign optimization tool and ageographical sharing map generation tool.

In an embodiment, the social structure manager 106 determines a secondmapping. (In this application, second mapping refers to a mappingbetween the users and a first mapping refers to a mapping between ananchor and a user). In another embodiment, the social structure manager106 constructs a social structure. Further, the web analytic server 104may extract data using various programming languages, such as StructuredQuery Language (SQL), 4D Query Language (4D QL), Object Query Language(OQL), and Stack Based Query Language (SBQL).

The domain web server 108 may correspond to a data storage system thathas the capability of storing the plurality of web pages 116. In anembodiment, the domain web server 108 hosts one or more of the web pagescorresponding to a plurality of content publishers or a plurality ofcontent providers. Examples of the plurality of content providers mayinclude, but are not limited to, forbes.com and mashable.com. Examplesof the plurality of content publishers include, but are not limited to,Facebook®, LinkedIn® and Stumble Upon®.

In an embodiment, the plurality of content publishers, also referred toas social channels in this disclosure, are the receivers of the sharingenabled by the tracking component 110. In an embodiment, a sharer sharesa URL to a social channel and a clicker clicks on the shared URL thatlead the clicker to a web page of a content publisher.

In an embodiment, the domain web server 108 subscribes to the webanalytic server 104 for one or more web analytic services. A web serviceprovider via the web analytic server 104 may host such services. Suchweb analytic services may include analysis based on the preferences of atarget audience, analysis based on an influential power of a user on theother users, analysis based on users with similar preferences, salesconversion analysis, and social quality index analysis for domainranking. The web page includes the tracking component 110.

The computing device 112 may correspond to a device capable of receivingan input from a user on a user interface displayed on a display screen.Examples of the computing device 112 may include, but are not limitedto, laptops, televisions, tablet computers, desktops, mobile phones,gaming consoles, and other such devices with a display screen thatdisplays the plurality of web pages 116. The computing device 112includes one or more browsing applications that enable the user tobrowse through a web page. The user provides the input, for example akeyword, to navigate through the content on the web page. Although threecomputing devices have been shown in FIG. 1, it may be appreciated thatthe disclosed embodiments can be implemented for a larger or smallernumber of computing devices. It may also be appreciated that for alarger number of computing devices, the web analytic server 104 may beimplemented as a cluster of computing devices configured to jointlyperform the functions of the web analytic server 104.

The database 114 corresponds to a storage device that stores datarequired to uncover relationships between users performing a useractivity in a networked environment. For example, the database 114 canstore information anchors associated with a plurality of users, trackingdata, publisher data, social structure, content categorization data,tracking log, and user activity data. The database 114 can beimplemented by using several technologies that are well known to thoseskilled in the art. Some examples of technologies include, but are notlimited to, Amazon Simple Storage Service (Amazon S3), Apache Hadoop™,Apache Hive™ and Apache PIG™.

In operation, the plurality of domain owners download and install atracking application in their respective domain web server 108. Thetracking component 110 tracks and gathers log records. The trackingcomponent 110 is configured to send the log records in real time to theweb analytic server 104 and the database 114.

In an embodiment, the database 114 includes a huge data storage bankthat stores the log records corresponding to one or more activities ofthe plurality of users on a web page related to the domain web server108.

In an embodiment, the web analytic server 104 determines the secondmapping based on the first mapping. The second mapping is determinedusing one or more of a user mapping data, the log records and the datastored in the database 114.

FIG. 2 is explained in conjunction with FIG. 1. FIG. 2 illustrates anexemplary system diagram showing various modules involved in theoperations of the web analytic server 104 in accordance with anembodiment. The web analytic server 104 includes a processor 202 and amemory 204. The processor 202 fetches and executes a set of instructionsstored in the memory 204. The processor 202 can be realized through anumber of processor technologies known in the art. Examples of theprocessor 202 can be X86 processor, RISC processor, ASIC processor, CSICprocessor, or any other processor. The memory 204 is configured to storea set of instructions or modules. Some of the commonly known memory 204implementations can be, but are not limited to, a random access memory(RAM), read only memory (ROM), hard disk drive (HDD), and secure digital(SD) card.

Further, the memory 204 includes a program module 206 and a program data208. The program module 206 includes a publisher management module 210,a tracking application module 212, a content categorization module 214,a user activity module 216, an anchor module 218, a user mapping module220, a social structure manager 106 and a weight assigning module 222.The program data 208 includes publisher data 224, tracking log 226,content categorization data 228, user activity data 230, anchor data232, user mapping data 234 and social structure 236.

The publisher management module 210 is configured to manage asubscription of the domain web server 108. The publisher managementmodule 210 stores the subscription information related to the pluralityof content publishers or content providers or domain owners as thepublisher data 224. Examples of the subscription information include,but are not limited to, details of a publisher, date and time of thetracking application subscription, time of expiry of the subscription,and pages at which the tracking application is installed.

The tracking application module 212 is configured to provide thetracking application to the plurality of content publishers or contentproviders on a subscription basis. The tracking component 110 located onthe plurality of web pages, is configured to receive the log recordscorresponding to the one or more activities of the plurality of users onthe web page. Examples of the one or more activities of the plurality ofusers may include, but are not limited to, viewing a web page andsharing through the tracking component 110. Examples of the log recordmay include, but are not limited to, an anonymous cookie representingthe one or more users, a time stamp, an event type, a sharing channel, acontent identifier, a domain information, and a browser agent.

The social structure manager 106 is configured to gather the log recordscorresponding to the one or more activities of the plurality of usersfrom the tracking component 110. Further, the social structure manager106 stores the gathered log records as the tracking log 226.

In another embodiment, the social structure manager 106 is configured togather the user mapping data 234 and determines the second mapping basedat least in part on the one or more activities of the plurality of usersand the anchor. Further, the social structure manager 106 stores thedetermined second mapping as the social structure 236. In anotherembodiment, the one or more activities of the plurality of users includesharing of content on the plurality of web pages 116 amongst the users.

In an embodiment, the second mapping can correspond to a socialstructure (e.g., sharing graph) that can be stored as the socialstructure 236. An exemplary sharing graph stored in the social structure236 is shown in FIG. 3 and is described in more detail below.

In an embodiment, social structure manager 106 provides a dashboard foran administrator operating the web analytic server 104. Further, thesocial structure manager 106 can also include tools for analysis basedon the log records, the social structure, etc. Social structure manager106 can also be configured to graphically represent the second mappingand display on a user interface.

In another embodiment, the administrator can define a time window fordetermining the second mapping. For example, the administrator, with theuse of the dashboard, can define a time window of a week, for which thesecond mapping can be determined.

In another embodiment, the social structure manager 106 instructs theweight assigning module 222 to assign numerical weights to graph edges.Further, the social structure manager 106 annotates the graph edges andthe nodes with metadata. In another embodiment, the social structuremanager 106 instructs the content categorization module 214 tocategorize the content on a web page.

The content categorization module 214 is configured to categorize thecontent on the web page into one or more content categories based on thelog records. Further, the categorized content is stored as the contentcategorization data 228.

The user activity module 216 determines the one or more activities ofthe plurality of users on the web page based on the log records.Further, the user activity module 216 stores the determined one or moreactivities of the plurality of users as the user activity data 230.

The anchor module 218 is configured to gather the log records from thetracking log 226. Further, the anchor module 218 determines an anchor onthe web page based on the one or more activities, wherein the anchor isutilized to perform the one or more activities. Furthermore, the anchormodule 218 stores the determined anchor as anchor data 232.

In an embodiment, the anchor module is configured to determine aplurality of anchors on the plurality of web pages based on the one ormore activities. Further, the plurality of anchors is utilized toperform the one or more activities.

The user mapping module 220, determines a first mapping between theanchor on the web page and the user based on the corresponding useractivity.

In an embodiment, the user mapping module 220 determines the firstmapping based on the corresponding user activity and a content categoryamongst the one or more content categories. Further, the user mappingmodule 220 stores the first mapping as the user mapping data 234.

The weight assigning module 222 is configured to assign numericalweights to graph edges. In an embodiment, the numerical weights areassigning to an edge connecting any two nodes based at least in part onthe activity of the two nodes. Further, the numerical weights areassigned to graph edges in order to quantify strength of connectionbetween the nodes associated with the graph edges. The numerical weightsmay be viewed as one of the many possible ways of annotating the graphedges. For example, during the time window of determining the secondmapping, if the numerical weights in the graph correspond to an event ofclicking a URL by the user, then the event of assigning the weight of 2to the edge of the graph would mean that there were two clicks performedby the user on the URL.

In another example, during the time window of determining the secondmapping, if a clicker clicks back on two shar.es URLs shared by the samesharer, a numerical weight of 2 is automatically assigned to an edgeconnecting the sharer and the clicker. This helps in differentiating theaforesaid instance from the others where the clicker clicks back on onlyone or a significant number of shar.es URLs shared by the sharer.

In an embodiment, a weighted graph could be assigned threshold valuessuch that the graph represents necessary information. For example, theweighted graph as discussed above may be assigned a threshold value of2. In an embodiment, the weighted graph could be pruned by removing theedges associated with negligible weights such that the weighted graphrepresents precise information.

In the example above the users who clicked the URL once would not beshown in the weighted graph but the users who clicked the URL twice andmore will be shown in the weighted graph. This helps in an effectiverepresentation of the necessary information in the weighted graph.

In an embodiment, assigning the numerical weights to the graph edges maybe useful for applications such as, but not limited to, influencermodeling and link prediction (i.e. prediction of existence of linksamong users).

FIG. 3 illustrates an embodiment of a sharing graph 300 stored in thesocial structure 236. Accordingly, the sharing graph 300 includes aplurality of nodes, node N1 represented as 302, node N2 represented as304, node N3 represented as 306, node N4 represented as 308, node N5represented as 310, node N6 represented as 312, node N7 represented as314, and node N8 represented as 316. The plurality of nodes 302, 304,306, 308, 310, 312, 314 and 316 are connected by a plurality of edges318, 320, 322, 324, 326 and 328. The node 306 is connected to the node302 by the edge 318. The node 308 is connected to the node 302 by theedge 320. The node 310 is connected to the node 302 by the edge 322. Thenode 312 is connected to the node 302 by the edge 324. The node 304 isconnected to the node 302 by the edge 326. The node 316 is connected tothe node 314 by the edge 328. (In this application, the nodes representusers and the edges represent anchors connecting the users).

In an embodiment, each of the plurality of edges 318, 320, 322, 324, 326and 328 represent an anchor.

In another embodiment, the nodes 304, 306, 310 and 314 perform anoperation of sharing on a web page. Therefore, the nodes 304, 306, 310and 314 are information sharers. In another embodiment, the nodes 308,312, and 316 perform the operation of clicking on the web page.Therefore, the nodes 308, 312 and 316 are information responders.Further, the node 302 performs the operation of both sharing andclicking on the web page. For example, the node 302 shares a linkassociated with a web page with the users, such as the nodes 308 and312. The node 302 clicks on a link associated with a web page that isshared by the nodes 304, 306 and 310. Therefore, the node 302 is aninformation sharer and an information responder.

In an embodiment, a shar.es URL associated with an event of sharing oran event of clicking performed by the plurality of users is associatedwith the plurality of edges connecting any two nodes. For example, theedge 326 has a shar.es URL “shar.es/bax81” associated with it. Theshar.es URL acts as the anchor in order to link the information sharerand the information responder. An alternative type of the anchor isillustrated by a hashed URL on the edges in FIG. 3, such as“000599f4b63c1c1b61702734cd93f605” (for the edge 320),“00169fb5bea57b2b182a0de192becfc0” (for the edge 322). In other words,the shar.es URL determines the link between the nodes involved in theevent of sharing, clicking or both. For example, the shar.es URL of theclicking event performed by the node 308 (clicker) is “shar.es/HbpgG”.The shar.es URL helps in tracking the node 302 (sharer) connected withthe node 308 (clicker).

On the other hand, the node 314 is a sharer that shared a shar.es URL“shar.es/HMiPz” to some user. But it is most unlikely to determine whoclicked on the shar.es URL shared by the node 314 since the sharingevent took place via a social networking website or any other websitethat does not disclose the identities of a user among the plurality ofusers. For example, some social channels have an opt-out option that theuser can choose to avoid being tracked. In the example, the user (e.g.,node 316) who clicked on the shar.es URL shared by the node 314 is ananonymous user.

In an embodiment, the sharing graph 300 as shown in FIG. 3 can have boththe nodes and the edges annotated with additional metadata. The nodescan be annotated with metadata such as, but not limited to, userinterests and/or user activities. The edges can be annotated withmetadata such as, but not limited to, category information for theanchor and/or relationship between users, etc so that the sharing graph300 can include more information for later use. The embodiment ofannotating the sharing graph 300 with metadata will be explained later.

FIG. 4 shows a flowchart illustrating a method for determining a mappingbetween a plurality of users accessing a plurality of web pages, inaccordance with an embodiment. FIG. 4 will be explained in conjunctionwith FIG. 1 and FIG. 2.

At step 402, log records are received from the tracking component 110.The social structure manager 106 receives the log records from thetracking component and stores it at the database 114. In an alternateembodiment, the social structure manager 106 retrieves the log recordsfrom the database 114 and stores it as the tracking log 226. The logrecord corresponds to the one or more activities of the plurality ofusers.

In an embodiment, the step 402 includes categorizing the content on theweb page into one or more content categories. The content categorizationmodule 214 gathers data from the tracking log 226 and categorizes thecontent on the web page associated with the corresponding log recordsinto the one or more content categories based on the log records. Inanother embodiment, the content categorization module 214 stores thecategorized content as the content categorization data 228.

In an embodiment, the step 402 further includes determining the one ormore activities of the plurality of users on the web page. The useractivity module 216 retrieves the content categorization data 228 anddetermines the one or more activities of the plurality of users on theweb page based on the log records. In another embodiment, the useractivity module 216 stores the determined one or more activities of theplurality of users as the user activity data 230.

In yet another embodiment, the step 402 further includes determining theanchor on the web page. The anchor module 218 retrieves the tracking log226 and determines the anchor on the web page based on the one or moreactivities. The anchor is utilized to perform the one or moreactivities. In another embodiment, the anchor module 218 stores thedetermined anchor as anchor data 232.

At step 404, the first mapping is determined by the user mapping module220 between the anchor on the web page and the user based on thecorresponding user activity. The first mapping is determined between theanchor data 232 and the user activity data 230. It is evident to aperson skilled in the art that a share cookie represents a sharer and aclick cookie represents a clicker. In an embodiment, share cookies andclick cookies are grouped together by Unified Resource Locator (URL), asshown in FIG. 5.

FIG. 5 shows share cookies S1 represented as 502 and S2 represented as506. Further, FIG. 5 shows click cookies C1 represented as 504, C2represented as 508, C3 represented as 510, C4 represented as 512, and C5represented as 514. In an embodiment, the share cookies (502 and 506)can have fields such as, but not limited to, a label for a share event,a label for a share cookie, a label for a share channel and a label fora share time. The click cookies (504, 508, 510, 512, and 514) can havefields such as, but not limited to, a label for a click event, a labelfor a click cookie, a label for a click channel and a label for a clicktime. In an embodiment, FIG. 5 shows the field “share cookie channeltimestamp” and the field “click cookie channel timestamp” respectivelyassociated with the share cookies (such as 502 and 506) and the clickcookies (such as 504, 508, 510, 512, and 514).

In an embodiment, the first mapping is based on the corresponding useractivity and the content category amongst the one or more contentcategories.

In an embodiment, the first mapping determined between the plurality ofanchors and the plurality of users is performed irrespective of anyinterest topic or content category. Therefore, it can be interpreted asa top-level mapping. Further, the top-level mapping can be extended torepresent a more specified set of users by extending the first mappingbetween the plurality of anchors and the plurality of users with thecategory information of the plurality of anchors. Consequently, thesecond mapping determined between the plurality of users will bedetermined based on the first mapping related to the categoryinformation. This leads to a more precise representation of a socialstructure.

Further, the first mapping is stored as the user mapping data 234. Thestep of determining the second mapping is explained in step 406.

At step 406, the social structure manager 106 gathers data from the usermapping module 220, and determines the second mapping between theplurality of users by linking the share cookie with the correspondingclick cookie and stores the second mapping as the social structure 236.The second mapping describes the relationship between a plurality ofsharers and clickers based on the sharing or clicking event. In anembodiment, the one or more activities of the plurality of users includesharing of content on the web page amongst the users. FIG. 6 a and FIG.6 b show an example of the linking made between the share cookie and theclick cookie with respect to a URL.

FIG. 6 a illustrates an input information 600 received by the webanalytic server 104 for linking the share cookies with the click cookieswith respect to the URL in accordance with an embodiment. Further, FIG.6 a is explained in conjunction with FIG. 5. Further, FIG. 6 a shows thelabel for a share channel, such as Facebook®, Twitter® and LinkedIn®associated with the share cookies and the click cookies. The sharecookies 502, 506 and the click cookies 504, 508, 510, 512, 514 arelinked using the tracking URL, in this case, the URL. FIG. 6 a showsthat the share cookies 502 and 506 shared the URL on Facebook®. Theshare channel (such as Facebook) is revealed from the label associatedwith the share cookies 502 and 504. Further, FIG. 6 a shows that theclick cookies 504, 510 and 514 clicked on the shar.es URL on Facebook®.The click cookie 508 clicked on the URL on Twitter® and the click cookie512 clicked on the URL on LinkedIn®. The label for a click channelassociated with the click cookies 504, 510 and 514 reveal that the URLwas clicked on by the click cookies via Facebook®. The label for a clickchannel associated with the click cookie 508 reveals that the URL wasclicked on by the click cookie via Twitter®. Further, the label for aclick channel associated with the click cookie 512 reveals that the URLwas clicked on by the click cookie via LinkedIn®.

FIG. 6 b illustrates an output cookie graph 602 generated by the webanalytic server 104 by linking the share cookies and the click cookieswith respect to the URL in accordance with an embodiment. Further, FIG.6 b is explained in conjunction with FIG. 5. The web analytic server 104processes the input information 600 and generates the output cookiegraph 602. A block 604 represents an all-paired cookie graph where theshare cookies 502 and 506 are respectively paired with all the clickcookies 504, 508, 510, 512, 514. In an embodiment, the pairing isperformed without the knowledge of a social channel that was used forsharing and without the knowledge of a time associated with the sharing.Therefore, there is an ambiguity associated with the output cookie graphrepresented by block 604 as the knowledge of the share channel isunknown. In order to eliminate the ambiguity, the share and the clickcookies are linked together based on the timestamp of a sharing eventand the sharing channel used. A block 606 represents the disambiguatedlinks generated between the share cookies and the click cookies based onthe channel and the timestamp used. In an embodiment, Facebook® is thechannel through which the sharing and the clicking event took placewhere a certain timestamp was recorded. For example, the share cookie502 shares the URL on Facebook®. The click cookies 504, 510, 514 clickon the URL shared by the share cookie 502 on Facebook®. Therefore, theshare cookie 502 is linked to the click cookies 504, 510 and 514 basedon the sharing channel and a timestamp recorded during the occurrence ofthe sharing or clicking event. In another example, the share cookie 506is linked to the click cookies 510 and 514 based on the sharing channeland a time stamp recorded during the occurrence of the sharing orclicking event. The disambiguation process during the generation of theoutput cookie graph 602 is further illustrated in FIG. 7.

FIG. 7 illustrates a method of disambiguating links between share andclick cookies by using URL as an anchor, in accordance with anembodiment. The elements shown in FIG. 6 b are similar to the elementsshown in FIG. 7. An all paired linkage shown in 604 (refer to FIG. 6 b)is represented in the form of a graph 700 in FIG. 7. Disambiguated linksgenerated between the share cookies and the click cookies based on thechannel and the timestamp as shown in 606 (refer to FIG. 6 b) isrepresented in the form of graph 702 in FIG. 7.

The graph 702 as shown in FIG. 7 could be incorporated with numericalweights such that a high degree of confidence on an edge between thesharer and the clicker can be achieved. This helps to reinforce reliablelinks between users.

FIG. 8 illustrates a method of assigning numerical weights to graphedges in accordance with an embodiment. FIG. 8 is shown to include ashare cookie S1 represented as 804 and click cookies C1 represented as806, C2 represented as 808, C3 represented as 810 and C4 represented as812. In the embodiment shown in 800, the click cookies 806, 808, 810 and812 have clicked on to the URL shared by the share cookie 804 through acertain channel. The click cookies 808 and 810 are shown to be repeatedseveral times as they have clicked back the share cookie S1 severaltimes. Therefore, an output 802 is generated which has the numericalweights added to the click cookies. In an embodiment, a measure ofnumerical weights is the frequency of clicks. Therefore, click cookies806 and 812 are assigned a weight of 1 as they have clicked back theshare cookie 804 once. The click cookie 808 is assigned a weight of 2 asit has clicked back the share cookie 804 twice. The click cookie 810 isassigned a weight of 3 as it has clicked back the share cookie 804thrice.

In an embodiment, the numerical weights assigned to graph edges can beformulated as a function of one or more factors. Examples of the one ormore factors can be, but are not limited to, a click back time after asharing event, a category information of URLs associated with an edgeassociating two nodes, a total number of shares for a sharer, a totalnumber of clicks for a clicker, a sharing channel, and a clickingchannel.

The method of assigning numerical weights to the graph edges can beadvantageous as it provides the opportunity to leverage the weightedgraph by adding threshold values. Further, it helps in achieving moreconfidence in representing the relationship between the sharers and theclickers and renders the weighted graph more reliable. The method ofassigning numerical weights to graph edges as shown in FIG. 8 is anexample annotation.

In an embodiment, the sharing graph 300 can include various types ofannotations on the nodes and edges. Various types of annotationsinclude, but are not limited to, labels, metadata, weights on edgesother than frequency, user interest topics, category information of theedges etc.

FIG. 9 illustrates a graph 900 showing a click back time within a firstday of a shared content, in accordance with an embodiment. The graph 900has the X-axis representing a click back time lag represented in hoursand the Y-axis representing cumulative percentage of total click backs.It is evident from the graph 900 that as the number of hours increases,the cumulative percentage of total click backs increases.

In an embodiment, graph 900 illustrates a likelihood of the users torespond to a sharing event within the first 24 hours. It is evident fromthe graph 900 that approximately 40% of click backs happen within thefirst hour of a time of occurrence of the sharing event. Further, thegraph 900 shows that 90% of click backs happen within the first 24 hoursof the time of occurrence of the sharing event. Therefore, it can beinferred from graph 900 that most users are likely to click back ashared content within the first day of the sharing event.

In another embodiment, graph 900 can be interpreted to provideheuristics for disambiguating the relationship between the sharer and/orthe clicker as shown in the block 606 (refer to FIG. 6 b) and 702 (referto FIG. 7). In an embodiment, if 90% of the click backs happen withinthe first 24 hours, then any clicks that happen after 24 hours of thetime of occurrence of the sharing event can be assigned a lowerconfidence weight accordingly.

FIG. 10 a shows statistics of average number of click events associatedwith a URL for the first mapping between the anchor and the user, inaccordance with an embodiment. The statistics 1000 has a logrepresentation of the number of click events per URL denoted in thex-axis and a log representation of the frequency of an occurrence of theclick event denoted in the y-axis. It can be inferred from thestatistics 1000 that as the number of click events per URL increases,the frequency of occurrence of the click event decreases. For example,let us consider the URL as http://www.google.com. The frequency of thenumber of times the URL was clicked once is higher than the frequency ofthe number of times the URL was clicked several times.

In an embodiment, it can be inferred from statistics 1000 that themajority of URLs will be clicked on no more than 10 times. However,there can be instances where some popular URLs may be clicked on 100times or more. The statistics 1000 shows only up to 100 clicks per URL.

FIG. 10 b shows statistics of average number of share events associatedwith a URL for the first mapping between the anchor and the user, inaccordance with an embodiment. The statistics 1002 has a logrepresentation of number of share events per URL denoted in the x-axisand a log representation of frequency of an occurrence of the shareevent denoted in the y-axis. It could be inferred from the statistics1002 that as the number of share events per URL increases, the frequencyof occurrence of the share event decreases. For example, let us considerthe URL as http://www.google.com. The frequency of the number of timesthe URL was shared once is higher than the frequency of the number oftimes the URL was shared several times.

In an embodiment, it can be inferred from statistics 1002 that themajority of URLs are shared no more than 10 times. However, there may beinstances where some popular URLs may be shared 100 times or more.

FIG. 11 a shows statistics of the number of inbound clicks associatedwith a user for the second mapping between the plurality of users, inaccordance with an embodiment. The statistics 1100 shows the number ofinbound links per cookie denoted in the x-axis and the frequency ofoccurrence of the event denoted in the y-axis. It could be interpretedfrom the statistics 1100 that as the number of inbound links per cookieincreases, the frequency of occurrence of the event decreases.

FIG. 11 b shows statistics of the number of outbound clicks associatedwith a user for the second mapping between the plurality of users, inaccordance with an embodiment. The statistics 1102 shows the number ofoutbound links per cookie denoted in the x-axis and the frequency ofoccurrence of the event denoted in the y-axis. It could be interpretedfrom the statistics 1102 that as the number of outbound links per cookieincreases, the frequency of occurrence of the event decreases.

FIG. 12 illustrates a method of constructing a social structureincrementally in accordance with an embodiment. FIG. 12 is explained inconjunction with FIG. 1 and FIG. 4. In an embodiment, step 1202 issimilar in functionality to step 402 (refer FIG. 4). At step 1202, logrecords are received from a tracking application at a pre-determinedtime window T2. In an embodiment, step 1204 is similar in functionalityto the step 404 (refer to FIG. 4). At step 1204, a first mapping betweenplurality of anchors and plurality of users is determined at thepre-determined time window T2. At step 1208, a pre-determined firstmapping up to a pre-determined time window T1 at step 1206 and thedetermined first mapping between plurality of anchors and plurality ofusers at step 1204 are merged together. Social structure manager 106(refer to FIG. 1) performs the process of merging anchor and usermappings. Finally, at step 1210, which is similar in functionality tostep 406 (refer to FIG. 4), a second mapping between the plurality ofusers is determined. The process of determination of the second mappingcorresponds to the construction of the social structure, such as asharing graph.

FIG. 13 illustrates an alternative method of constructing a socialstructure incrementally in accordance with yet another embodiment. In anembodiment, step 1302 is similar in functionality to step 402 (refer toFIG. 4). Step 1304 is similar in functionality to the step 404 (refer toFIG. 4). Further, step 1306 is similar in functionality to the step 406(refer to FIG. 4). The steps 1302, 1304 and 1306 are performed at apre-determined time window T2. At step 1310, a pre-determined secondmapping determined at step 1308 at a pre-determined time window T1 ismerged with the determined second mapping at step 1306. Social structuremanager 106 (refer to FIG. 1) performs the process of merging multiplesecond mappings in step 1310. In an embodiment, the multiple secondmappings merged at step 1310 corresponds to the social structure, suchas a social graph.

In an example implementation, the user visits a web pagewww.x11y22z33.com that displays content related to car sales in aparticular geographical region. The content categorization module 214categorizes the content on the website and categorizes it as a categorynamed “automotive”. In an embodiment, the categorized content couldfurther be categorized as “sales” under the category “automotive”.Further, the user activity module 216 determines the activity of theuser, which in this case is viewing the web page. The user activitymodule 216 stores the corresponding user activity as the user activitydata 230. Further, the anchor module 218 determines the anchor, such asthe shortened URL associated with the web page based on the useractivity of viewing the web page. The determined anchor is stored asanchor data 232. Further, the user mapping module 220 determines thefirst mapping based on the shortened URL and the user activity of theuser viewing the web page, and the first mapping is stored as usermapping data 234. Furthermore, the social structure manager 106 gathersthe user mapping data 234, and determines the second mapping thatuncovers the relationship between the users, basically, the sharer andthe clicker. The second mapping corresponds to the social structure.

One of the advantages of incrementally constructing a social structureas shown in FIGS. 12 and 13 is that an updated social structure need notbe built from scratch. An existing social structure could be updatedwith new data to form an updated social structure by leveraging atimestamp. For example, grouping share and click cookies by URL (referto FIG. 5) every day in a week would result in seven different URLgraphs. In other words, there would be seven different daily URL graphs,also referred to as a weekly URL graph. In order to incrementallyconstruct the social structure, a daily URL graph of day 8 is mergedwith the weekly URL graph. The resultant structure is the updated socialstructure.

The disclosed methods and systems, as described in the ongoingdescription or any of its components, may be embodied in the form of acomputer system. Typical examples of a computer system includes, but arenot limited to, a general-purpose computer, a programmed microprocessor,a micro-controller, a peripheral integrated circuit element, and otherdevices or arrangements of devices that are capable of implementing thesteps that constitute the method of the present invention.

The computer system comprises a computer, an input device, and a displayunit. The computer further comprises a microprocessor. Themicroprocessor is connected to a communication bus. The computer alsoincludes a memory. The memory may be Random Access Memory (RAM) or ReadOnly Memory (ROM). The computer system further comprises a storagedevice, which may be a hard-disk drive or a removable storage drive,such as a floppy-disk drive, optical-disk drive, etc. The storage devicemay also be other similar means for loading computer programs or otherinstructions into the computer system. The computer system also includesa communication unit. The communication unit allows the computer toconnect to other databases and the Internet through an Input/output(I/O) interface, allowing the transfer as well as reception of data fromother databases. The communication unit may include a modem, an Ethernetcard, or any other similar device, which enables the computer system toconnect to databases and networks, such as LAN, MAN, WAN and theInternet. The computer system facilitates inputs from a user throughinput device, accessible to the system through an I/O interface.

The computer system executes a set of instructions that are stored inone or more storage elements in order to process input data. The storageelements may also hold data or other information as desired. The storageelement may be in the form of an information source or a physical memoryelement present in the processing machine.

The programmable or computer readable instructions may include variouscommands that instruct the processing machine to perform specific taskssuch as the steps that constitute the method of the present invention.The method and systems described can also be implemented using onlysoftware programming or using only hardware or by a varying combinationof the two techniques. The disclosed invention is independent of theprogramming language used and the operating system in the computers. Theinstructions for the invention can be written in all programminglanguages including, but not limited to ‘C’, ‘C++’, ‘Visual C++’,‘Visual Basic’, Java and python. Further, the software may be in theform of a collection of separate programs, a program module with alarger program or a portion of a program module, as in the presentinvention. The software may also include modular programming in the formof object-oriented programming. The processing of input data by theprocessing machine may be in response to user commands, results ofprevious processing or a request made by another processing machine. Theinvention can also be implemented in all operating systems and platformsincluding, but not limited to, ‘Unix’, ‘Windows Operating System’,‘Android’, ‘Mac OS’, and ‘Linux’.

The programmable instructions can be stored and transmitted on computerreadable medium. The programmable instructions can also be transmittedby data signals across a carrier wave. The disclosed invention can alsobe embodied in a computer program product comprising a computer readablemedium, the product capable of implementing the above methods andsystems, or the numerous possible variations thereof.

While various embodiments have been illustrated and described, it willbe clear that the invention is not limited to these embodiments only.Numerous modifications, changes, variations, substitutions andequivalents will be apparent to those skilled in the art withoutdeparting from the spirit and scope of the invention as described in theclaims.

What is claimed is:
 1. A method for determining a social relationshipbetween a sharer and a clicker of a plurality of users accessing aplurality of web pages, wherein the social relationship between thesharer and the clicker is explicitly not defined, the method comprising:receiving at least one log record from a tracking component located onthe plurality of web pages, the at least one log record corresponding toone or more activities of the plurality of users on the plurality of webpages, wherein the sharer shares an information entity on the pluralityof web pages and the clicker clicks on the information entity shared bythe sharer; determining a first mapping between a plurality of anchorson the plurality of web pages and the plurality of users based on thecorresponding one or more activities, the plurality of anchors beingutilized to perform the one or more activities, wherein the activitiesinclude sharing activities and click-based activities, and wherein thesharing activities and the click-based activities having correspondingtimestamps are performed on a web page of the plurality of web pages;determining a second mapping between the sharer and the clicker based onthe first mapping during a first pre-determined time window, wherein thesecond mapping represents a social relationship between the sharer andthe clicker when the sharer and the clicker perform the correspondingsharing and click-based activities on an anchor of the plurality ofanchors of the web page, and wherein the clicker performs theclick-based activity after the sharer performs the sharing activity;generating a sharing graph which comprises a plurality of nodes and aplurality of edges connecting the plurality of nodes, wherein thesharing graph is a visual representation of the social relationship,wherein the plurality of nodes correspond to the plurality of users andthe plurality of edges correspond to the plurality of anchors, whereinthe plurality of edges have corresponding numerical weights, socialchannel labels, and timestamp labels assigned thereto, wherein thesharing graph is pruned based on the numerical weights, and wherein thesharing graph corresponds to a graphical representation of linksprevailing among the plurality of users, the links indicating that thesocial relationship is based on at least one of similar interest,proximity of locations, and communication connections.
 2. The method ofclaim 1, further comprising installing a tracking application in adomain web server hosting the plurality of web pages.
 3. The method ofclaim 1, wherein the one or more activities of the plurality of usersfurther include one of: viewing a web page and sharing through thetracking component.
 4. The method of claim 1, further comprisingupdating the sharing graph based on the timestamps.
 5. The method ofclaim 1, wherein determining the first mapping further comprisesincrementally constructing a pre-determined first mapping after a secondpre-determined time window.
 6. The method of claim 1, whereindetermining the second mapping further comprises incrementallyconstructing a pre-determined second mapping after a thirdpre-determined time window.
 7. The method of claim 1, wherein thetracking component comprises one or more of: a widget, a button, ahypertext, a web beacon, and a link on the plurality of web pages. 8.The method of claim 1, wherein the plurality of anchors comprises one ormore of: a shortened unified resource locator (URL), an event trackingURL, a shared URL, a copy and paste text snippet, and an interest topic.9. The method of claim 1, further comprising categorizing one or morecontent on the plurality of web pages, associated with the at least onelog record, into one or more content categories, wherein thecategorization is based on the at least one log record.
 10. The methodof claim 9, wherein the first mapping is based on the one or moreactivities and the one or more content categories.
 11. The method ofclaim 1, wherein the at least one log record comprises one or more of:an anonymous cookie representing at least one of the sharer and theclicker, a timestamp, an event type, a sharing channel, a contentidentifier, domain information, and a browser agent.
 12. The method ofclaim 1, further comprising annotating the plurality of nodes and theplurality of edges with metadata information, wherein the metadatainformation comprises one or more of: a user interest, a user activity,category information of the plurality of anchors, a click back timeafter the sharing activity, a total number of shares, a total number ofclicks, a sharing channel, and a clicking channel.
 13. A system fordetermining a social relationship between a sharer and a clicker of aplurality of users accessing a plurality of web pages, wherein thesocial relationship between the sharer and the clicker is explicitly notdefined, the system comprising one or more processors configured to:receive at least one log record corresponding to one or more activitiesof the plurality of users on the plurality of web pages, wherein thesharer shares an information entity on the plurality of web pages andthe clicker clicks on the information entity shared by the sharer;determine a first mapping between a plurality of anchors on theplurality of web pages and the plurality of users based on thecorresponding one or more activities, the plurality of anchors beingutilized to perform the one or more activities, wherein the activitiesinclude sharing activities and click-based activities, and wherein thesharing activities and the click-based activities having correspondingtimestamps are performed on a web page of the plurality of web pages;determine a second mapping between the sharer and the clicker based onthe first mapping during a first pre-determined time window, wherein thesecond mapping represents a social relationship between the sharer andthe clicker when the sharer and the clicker perform correspondingsharing and click-based activities on an anchor of the plurality ofanchors of the web page, and wherein the clicker performs theclick-based activity after the sharer performs the sharing activity; andgenerate a sharing graph which comprises a plurality of nodes and aplurality of edges connecting the plurality of nodes, wherein thesharing graph is a visual representation of the social relationship,wherein the plurality of nodes correspond to the plurality of users andthe plurality of edges correspond to the plurality of anchors, whereinthe plurality of edges have corresponding numerical weights, socialchannel labels, and timestamp labels assigned thereto, wherein thesharing graph is pruned based on the numerical weights, and wherein thesharing graph corresponds to a graphical representation of linksprevailing among the plurality of users, the links indicating that thesocial relationship is based on at least one of similar interest,proximity of locations, and communication connections.
 14. The system ofclaim 13, wherein the one or more processors are further configured tomanage a subscription of a plurality of domain web servers to thesystem, the plurality of domain web servers hosting the plurality of webpages.
 15. The system of claim 13, wherein the one or more processorsare further configured to determine the one or more activities of theplurality of users on the plurality of web pages based on the at leastone log record.
 16. The system of claim 13, wherein the one or moreprocessors are further configured to categorize content on the pluralityof web pages into one or more content categories.
 17. The system ofclaim 13, wherein the plurality of anchors comprises one or more of: ashortened unified resource locator (URL), an event tracking URL, ashared URL, a copy and paste text snippet, and an interest topic. 18.The system of claim 13, wherein the at least one log record comprisesone or more of: an anonymous cookie representing at least one of thesharer and the clicker, a timestamp, an event type, a sharing channel, acontent identifier, domain information, and a browser agent.
 19. Thesystem of claim 13, further comprising annotating the plurality of nodesand the plurality of edges with metadata information, wherein themetadata information comprises one or more of: a user interest, a useractivity, category information of the plurality of anchors, a click backtime after the sharing activity, a total number of shares, a totalnumber of clicks, a sharing channel, and a clicking channel.
 20. Acomputer program product for use with a computer, the computer programproduct comprising a non-transitory computer usable medium having acomputer readable program code embodied therein for determining amapping between a plurality of users accessing a plurality of web pages,the computer readable program code comprising a set of instructions for:receiving at least one log record from a tracking component located onthe plurality of web pages, the at least one log record corresponding toone or more activities of the plurality of users on the plurality of webpages, wherein the sharer shares an information entity on the pluralityof web pages and the clicker clicks on the information entity shared bythe sharer; determining a first mapping between a plurality of anchorson the plurality of web pages and the plurality of users based on thecorresponding one or more activities, the plurality of anchors beingutilized to perform the one or more activities, wherein the activitiesinclude sharing activities and click-based activities, and wherein thesharing activities and the click-based activities having correspondingtimestamps are performed on a web page of the plurality of web pages;determining a second mapping between the sharer and the clicker based onthe first mapping during a first pre-determined time window, wherein thesecond mapping represents a social relationship between the sharer andthe clicker when the sharer and the clicker perform correspondingsharing and click-based activities on an anchor of the plurality ofanchors of the web page, and wherein the clicker performs theclick-based activity after the sharer performs the sharing activity; andgenerating a sharing graph which comprises a plurality of nodes and aplurality of edges connecting the plurality of nodes, wherein thesharing graph is a visual representation of the social relationship,wherein the plurality of nodes correspond to the plurality of users andthe plurality of edges correspond to the plurality of anchors, whereinthe plurality of edges have corresponding numerical weights, socialchannel labels, and timestamp labels assigned thereto, wherein thesharing graph is pruned based on the numerical weights, and wherein thesharing graph corresponds to a graphical representation of linksprevailing among the plurality of users, the links indicating that thesocial relationship is based on at least one of similar interest,proximity of locations, and communication connections.
 21. The computerprogram product of claim 20, wherein the plurality of anchors comprisesone or more of: a shortened unified resource locator (URL), an eventtracking URL, a shared URL, a copy and paste text snippet, and aninterest topic.
 22. The computer program product of claim 20, whereinthe at least one log record comprises one or more of: an anonymouscookie representing at least one of the sharer and the clicker, atimestamp, an event type, a sharing channel, a content identifier,domain information, and a browser agent.
 23. The computer programproduct of claim 20, further comprising annotating the plurality ofnodes and the plurality of edges with metadata information, wherein themetadata information comprises one or more of: a user interest, a useractivity, category information of the plurality of anchors, a click backtime after the sharing activity, a total number of shares, a totalnumber of clicks, a sharing channel, and a clicking channel.